reach's Introduction

reach

A light-weight package for working with pre-trained word embeddings. Useful for input into neural networks, or for doing compositional semantics.

reach can read in word vectors in word2vec or glove format without any preprocessing.

The assumption behind reach is a no-hassle approach to featurization. The vectorization and bow approaches know how to deal with OOV words, removing these problems from your code.

reach also includes nearest neighbor calculation for arbitrary vectors, and composition functions for lists of items.

Example

import numpy as np

from reach import Reach

# Load from a .vec or .txt file
# unk_word specifies which token is the "unknown" token.
# If this is token is not in your vector space, it is added as an extra word
# and a corresponding zero vector.
# If it is in your word, it is used.
r = Reach.load("path/to/embeddings", unk_word="UNK")

# Alternatively, if you have a matrix, you can directly
# input it.

# Stand-in for word embeddings
mtr = np.zeros((2, 300))
words = ["cat", "dog"]
r = Reach(mtr, words)

# Get vectors through indexing.
# Throws a KeyError if a word is not present.
vector = r['cat']

# Compare two words.
similarity = r.similarity('cat', 'dog')

# Find most similar.
similarities = r.most_similar('cat', 5)

sentence = 'a dog is the best creature alive'.split()
corpus = [sentence, sentence, sentence]

# bow representation consistent with word vectors,
# for input into neural network.
bow = r.bow(sentence)

# vectorized representation.
vectorized = r.vectorize(sentence)

# can remove OOV words automatically.
vectorized = r.vectorize(sentence, remove_oov=True)

# vectorize corpus.
transformed = r.transform(corpus)

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